Explainable AI in Disease Diagnosis

With the advancement of Artificial Intelligence (AI) techniques, intelligent healthcare applications, such as early disease diagnosis, treatment of rare diseases, diagnosis using images, and clinical decision support systems, have become more ever-demanding. Disease Diagnosis (DD) is the most crucial area which needs early detection. In countries having voluminous populations and comparatively few healthcare professionals, there is a dire need for AI-enabled systems using Machine Learning (ML) and Deep Learning (DL) models for the fast and timely detection of diseases. Though ML-/DL-based systems have shown excellent performance across various domains, DD is still in the nascent stages of adaptability among medical practitioners. The leading cause is their lack of trust in black-box approaches that involve complex computations in their hidden layers. Explainable Artificial Intelligence (XAI), a relatively new field of AI, explains or interprets the recommendations generated by ML/DL models. Interpretability, tractability and explainability of machine-generated recommendations are essential in healthcare, especially in DD. This chapter discusses XAI post-hoc algorithms, such as Local Interpretable Model Agnostic Explanation, Partial Dependence Plot, Shapley Additive Explanations, and Gradient Class Activation Map, that explain ML-/DL-generated recommendations for DD. These explanations may help medical practitioners visualise the features contributing to algorithm-generated decisions. Consequently, it reduces errors during diagnosis and makes the system more trustworthy. The chapter explains the implementation of XAI methods using case studies on COVID-19 and Cancer diagnosis. XAI has shown promising outcomes. Yet, there is much scope for further exploration and research. The paper, therefore, discusses various limitations of XAI and challenges.